public Dictionary <string, double> Classify(FuzzyTable table, int rowId, List <Rule> fuzzyRules)
{
var classAttribs = table.getClassAttribute();
// K2 Ak min {1.0, 0.9} Potom C is c1, find min
var Ei = new Dictionary <Rule, double>();
foreach (var rule in fuzzyRules)
{
var membershipDegree = double.MaxValue;
foreach (var attr in rule.Items)
{
var value = table.getData(attr.Id, rowId);
if (value < membershipDegree)
{
membershipDegree = value;
}
}
if (!Ei.ContainsKey(rule))
{
Ei.Add(rule, membershipDegree);
}
}
//K3
var gcj = new Dictionary <string, List <Rule> >();
for (var i = 0; i < table.getClassAttribute().Labels.Length; i++)
{
gcj[table.getClassAttribute().Labels[i].Id.ToString()] = new List <Rule>();
}
foreach (var rule in fuzzyRules)
{
gcj[rule.C.Id].Add(rule);
}
//K4 Ak max{0.9, 0, 0, 0.4} Potom C is c1
var returnValue = new Dictionary <string, double>();
foreach (var classAttr in classAttribs.Labels)
{
returnValue.Add(classAttr.Id.ToString(), double.MinValue);
}
foreach (var cj in gcj.Keys)
{
foreach (var rule in gcj[cj])
{
var value = Ei[rule];
if (value > returnValue[rule.C.Id])
{
returnValue[rule.C.Id] = value;
}
}
}
return(returnValue);
}
private bool ExistsAtLeastOneRuleForEachClassAttribute(FuzzyTable table, List <Rule> rules)
{
foreach (var classAttr in table.getClassAttribute().Labels)
{
var classAttrExistsInRules = false;
foreach (var rule in rules)
{
if (rule.C.Id.Equals(classAttr.Id.ToString()))
{
classAttrExistsInRules = true;
break;
}
}
if (!classAttrExistsInRules)
{
return(false);
}
}
return(true);
}
public ConfusionMatrix Validate(int numberOfFolds, FuzzyTable fuzzyTable, IProcessable algorithm, double tolerance = .5)
{
int instancesSize = fuzzyTable.GetTable().Rows.Count;
ArrayList[] foldsInstances = new ArrayList[numberOfFolds];
for (int i = 0; i < numberOfFolds; i++)
{
foldsInstances[i] = new ArrayList();
}
int numberOfClassValues = fuzzyTable.getClassAttribute().Labels.Length;
FuzzyAttributeLabel[] classValues = new FuzzyAttributeLabel[numberOfClassValues];
for (int i = 0; i < numberOfClassValues; i++)
{
classValues[i] = fuzzyTable.getClassAttribute().Labels[i];
}
double[] countClass = getClassValuesNumber(classValues, numberOfClassValues, fuzzyTable);
int foldSize = instancesSize / numberOfFolds;
double[] foldClassSize = new double[countClass.Length];
for (int i = 0; i < foldClassSize.Length; i++)
{
double perc = countClass[i] / (double)instancesSize;
foldClassSize[i] = (foldSize * perc);
}
var dataCountInOneReplication = fuzzyTable.DataCount() / numberOfFolds; // the size of the fold
var confusionMatrix = new ConfusionMatrix();
var noDataTable = fuzzyTable.CloneNoData();
var rngIndexes = getRNGIndexes(instancesSize);
for (int i = 0; i < numberOfFolds; i++)
{
var instancesAdded = new ArrayList(foldSize); // what will be deleted
double[] foldClassSizeAdded = new double[foldClassSize.Length];
foreach (int index in rngIndexes)
{
var label1Value = this.getData(fuzzyTable, classValues[0], index); // e.g c1= 0.8
var label2Value = this.getData(fuzzyTable, classValues[1], index); // e.g c2= 0.2
if (label1Value > 0.5) // c1 > 0.5
{
if (foldClassSizeAdded[0] < foldClassSize[0])
{
foldClassSizeAdded[0] += label1Value;
foldsInstances[i].Add(index); // add the index to the fold
instancesAdded.Add(index);
}
}
else // c2 > 0.5
{
if (foldClassSizeAdded[1] < foldClassSize[1])
{
foldClassSizeAdded[1] += label2Value;
foldsInstances[i].Add(index); // add the index to the fold
instancesAdded.Add(index);
}
}
if (foldClassSizeAdded[0] >= foldClassSize[0] && foldClassSizeAdded[1] >= foldClassSize[1])
{
break;
}
}
// remove indexes that were used in this fold
foreach (var item in instancesAdded)
{
rngIndexes.Remove(item);
}
}
// now i have the folds
for (var fold = 0; fold < numberOfFolds; fold++)
{
var table = (FuzzyTable)fuzzyTable.CloneNoData();
var testDataTable = (FuzzyTable)table.CloneNoData();
addFoldsDataToTableAndTestTable(table, testDataTable, foldsInstances, fold, numberOfFolds, fuzzyTable);
algorithm.init(table);
var rules = algorithm.process();
if (!ExistsAtLeastOneRuleForEachClassAttribute(table, rules))
{
return(null);
}
CalculateResultForRules(testDataTable, rules, confusionMatrix, tolerance);
}
// Console.WriteLine("Accuracy: "+confusionMatrix.Accuracy());
// Console.WriteLine("Sensitivity: "+confusionMatrix.Sensitivity());
// Console.WriteLine("Specificity: "+confusionMatrix.Specificity());
// Console.WriteLine("Precision: "+confusionMatrix.Precision());
// Console.WriteLine("Krit: "+confusionMatrix.Criteria());
// Console.WriteLine("Kriteria: "+(confusionMatrix.Sensitivity() + confusionMatrix.Specificity()) / 2);
confusionMatrix.CalculatePercentNumbers();
return(confusionMatrix);
}